Synthetic Aperture Radar (SAR) is frequently employed in remote sensing due to its ability to form high-resolution images with relative invariance to weather and lighting conditions. SAR images are formed using a moving radar that collects data over a scene from multiple perspectives. The resulting data sets are complex-valued, with the magnitude corresponding to the reflected signal intensity of the scene and the phase indicating scattering properties.
One application of SAR is change detection, which utilizes two SAR data collections of the same scene at different times to infer changes that have occurred between the data collections. Traditional SAR change detectors employ either (1) non-coherent intensity change detection, or (2) coherent change detection. Traditional non-coherent intensity change detection utilizes changes in the magnitude of SAR images to indicate large-scale changes, such as the appearance of a sizeable object during the second data collection that was not present during the first. Traditional coherent change detection (CCD) uses SAR phase as well as magnitude to estimate the coherence between the two SAR images. CCD requires the two image collections to use identical collection geometries, so that each respective image phase is aligned, leading to the detection of smaller-scale changes, such as those made by a vehicle driving on a soft surface.
Traditional coherence magnitude detectors are biased, particularly when the true coherence is small. Although this bias can be reduced by an increase in the number of samples, in practice, increasing the number of samples for each spatial location in a pair of SAR images reduces the effective spatial resolution of the resulting CCD image, making detection of small-scale changes more difficult. Furthermore, as the size of the sample window increases, the assumption that the samples are drawn independently from the same distribution is less likely to be met. Therefore, there is a need for improved SAR change detection.